A statistical framework based on a family of full range autoregressive models for edge extraction
Pattern Recognition Letters
On image reconstruction algorithms for binary electromagnetic geotomography
Theoretical Computer Science
A new method for parameter estimation of edge-preserving regularization in image restoration
Journal of Computational and Applied Mathematics
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Adaptive total variation denoising based on difference curvature
Image and Vision Computing
Fast MAP-based multiframe super-resolution image reconstruction
Image and Vision Computing
Estimation of the parameters in regularized simultaneous super-resolution
Pattern Recognition Letters
Indirect density estimation using the iterative Bayes algorithm
Computational Statistics & Data Analysis
Convex Approximation Technique for Interacting Line Elements Deblurring: a New Approach
Journal of Mathematical Imaging and Vision
Machine Vision and Applications
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The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the a posteriori log-likelihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography